Medical imaging technologies such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT), or Positron Emission Tomography (PET) are able to acquire information about the physical structure of a patient and construct two dimensional or three dimensional data sets. These data sets can then be used to construct visualizations of the data sets in a form which is useful for diagnosis by a physician.
For example, a magnetic field is used by MRI scanners to align the nuclear spins of atoms as part of the procedure for producing images within the body of a patient. This magnetic field is referred to as the B0 field. During an MRI scan, Radio Frequency (RF) pulses generated by a transmitter coil cause perturbations to the local magnetic field and can be used to manipulate the orientation of the nuclear spins relative to the B0 field. RF signals emitted by the nuclear spins are detected by a receiver coil, and these RF signals are used to construct the MRI images.
For many clinical applications, regions of interest are delineated by an operator in the medical image data to aid the physician with his or her diagnosis of the patient. For MRI, examples of applications include:
Measuring aneurysm, tumor, or brain size. This information is often tracked over a period of time by performing multiple MRI examinations.
Defining a volume in the medical image data containing a blood vessel structure before making a Maximum Intensity Projection.
Automatically delineating nerve fiber bundles.
Delineating regions of interest is usually done manually by the operator and as such is a tedious job. Different operators will draw regions of interest in a different way leading to inter-operator variability. Furthermore, a single operator may delineate the regions of interest inconsistently over time, or between different patients leading to intra-operator variability. Such variability reduces the usefulness of diagnostic data when consistency is required, such as when the size of a tumor needs to be tracked over multiple examinations.
Alternatively, automated identification of regions of interest can be used to delineate an anatomical structure. However, the clinical delineation of these regions of interest can differ depending upon the preference of the radiologist with respect to shape, size, location. For example, the region of interest boundary may be located in between contrast boundaries, which are typically extracted using a segmentation algorithm. Furthermore automated segmentation algorithms only function properly if the image used for the automated segmentation has the contrast characteristics required by the segmentation algorithm.
U.S. Patent application 2008/0103383 describes acquiring an MRI image that is optimized for a particular target structure along with an alignment scout image.